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README.md
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# FeynModel V 0.1
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/645364cbf666f76551f93111/RNgPbOsYh_XAo-rCRrNUb.png)
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# how to use
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```python
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from transformers import AutoProcessor, AutoModelForCausalLM
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model_id='Imagroune/feynmodel'
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processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_id,trust_remote_code=True)
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model.to('cuda')
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```
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# LLM Inference
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```python
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input_text = "<start_of_turn>user\nCombien d'helicoptère un humain adulte peut manger en un seul repas?.<end_of_turn> <start_of_turn>model\n"
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input_ids = processor.tokenizer(input_text, return_tensors="pt").to("cuda")
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# Génération du texte en mode streaming
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max_length = input_ids.input_ids.shape[1] + 1024 # Longueur maximale totale
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stream_output = [] # Liste pour stocker le flux de sortie
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# Génération et affichage en mode streaming
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for output in model.generate(input_ids=input_ids.input_ids,max_length=max_length, do_sample=True, temperature=0.7):
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decoded_output = processor.tokenizer.decode(output, skip_special_tokens=True)
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stream_output.append(decoded_output)
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print(decoded_output, end="", flush=True)
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```
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# Vision Inference
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList
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class PrintTokensStoppingCriteria(StoppingCriteria):
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def __init__(self, tokenizer):
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self.tokenizer = tokenizer
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def __call__(self, input_ids, scores, **kwargs):
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# Decode the last generated token and print it
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last_token_id = input_ids[0, -1].item()
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token = self.tokenizer.decode([last_token_id], skip_special_tokens=True)
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print(token, end='', flush=True)
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# Continue generating tokens until a stopping condition is met
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# Return True to stop, False to continue
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return False
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stopping_criteria = PrintTokensStoppingCriteria(processor.tokenizer)
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from PIL import Image
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import requests
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input_text = "<start_of_turn>user\n what is this ?<end_of_turn>\n<start_of_turn>model"
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
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image = Image.open(requests.get(url, stream=True).raw)
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input_text="""<start_of_turn>user
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Create a concise caption that accurately describes the main elements in the image provided
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<end_of_turn>
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<start_of_turn>model
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"""
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inputs = processor(text=input_text, images=image, return_tensors="pt")
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inputs = {key: value.cuda() for key, value in inputs.items()}
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image
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```
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/645364cbf666f76551f93111/XVxraj69m26HtkfaWQhve.png)
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```python
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max_length =inputs['input_ids'].shape[1] + 1024 # Longueur maximale totale
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stream_output = [] # Liste pour stocker le flux de sortie
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# Génération et affichage en mode streaming
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ret= model.generate(inputs['input_ids'], pixel_values=inputs['pixel_values'],stopping_criteria=StoppingCriteriaList([stopping_criteria]),max_length=2048, do_sample=True, temperature=0.7)
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# An older, green car sits parked on the curb in front of a building.
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```
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